Dynamic Community Detection into Analyzing of Wildfires Events

TL;DR

This paper applies a novel dynamic community detection method to analyze wildfire events in the Amazon, revealing patterns and insights into wildfire dynamics over time using network science techniques.

Contribution

It introduces a two-phase dynamic community detection approach using temporal networks and the Louvain algorithm to study wildfire patterns, a novel application in environmental network analysis.

Findings

Dynamic communities reveal wildfire patterns throughout the year.

The approach uncovers temporal changes in wildfire activity.

Community structures correlate with wildfire events in the Amazon.

Abstract

The study and comprehension of complex systems are crucial intellectual and scientific challenges of the 21st century. In this scenario, network science has emerged as a mathematical tool to support the study of such systems. Examples include environmental processes such as wildfires, which are known for their considerable impact on human life. However, there is a considerable lack of studies of wildfire from a network science perspective. Here, employing the chronological network concept -- a temporal network where nodes are linked if two consecutive events occur between them -- we investigate the information that dynamic community structures reveal about the wildfires' dynamics. Particularly, we explore a two-phase dynamic community detection approach, i.e., we applied the Louvain algorithm on a series of snapshots. Then we used the Jaccard similarity coefficient to match communities across adjacent snapshots. Experiments with the MODIS dataset of fire events in the Amazon basing were conducted. Our results show that the dynamic communities can reveal wildfire patterns observed throughout the year.